The present application relates to image reconstruction, such as may be performed by computed tomography (CT) scanners, for example. It finds particular use in medical, security, and/or industrial applications where image data is reconstructed from projection data using an iterative reconstruction technique.
Radiographic imaging systems, such as CT systems, provide information and/or images of an object under examination or interior aspects of the object. For example, in radiographic imaging systems, the object is exposed to radiation, and one or more images are formed based upon the radiation absorbed by the object or an amount of radiation that is able to pass through the object. Typically, highly dense objects absorb (e.g., attenuate) more radiation than less dense objects, and thus an object having a higher density, such as a bone or metal plate, for example, will appear differently than less dense objects, such as skin or clothing, for example.
Today, there are numerous types of radiographic imaging systems. One of the more versatile types of radiographic imaging systems is a CT scanner. CT scanners are used in a variety of fields and are used to identify a plurality of characteristics and/or features about an object under examination. For example, security CT scanners can determine the chemical composition of an object under examination and/or can identify markers (e.g., density, shape, etc.) that are commonly associated with threat items. In medical applications, CT scanners can be used to generate images of a variety of aspects of a patient. For example, some CT scanners are configured to distinguish between gray matter and white matter in the brain, while other CT scanners are configured to image arteries and/or the heart.
One of the features that have made CT scanners so versatile is their ability to record X-ray projections (e.g., views) of an object from a plurality of positions (e.g., generally covering at least a 180 degree angular range) and to employ an image reconstruction algorithm(s) to generate two-dimensional and/or three-dimensional images indicative of the object.
To view an object under examination from a plurality of angles, a typical CT scanner comprises a radiation source and a detector array that are mounted on a rotating gantry comprising a central opening (e.g., a bore) large enough to receive the object (e.g., a patient, luggage, etc.). During the examination of the object, the rotating gantry, including the radiation source and/or the detector array, is rotated about the object and radiation is emitted from the radiation source. By rotating the radiation source relative to the object, radiation is projected through the object from a plurality of different positions, and the detector array generates measured projection data indicative of the amount of radiation it detects.
Image reconstruction algorithms are used to reconstruct volumetric images (e.g., density images, z-effective images, etc.) from the projection data generated by the detector array. Today, image reconstruction algorithms can be broadly classified into analytical algorithms and iterative algorithms.
Analytical image reconstruction algorithms, such as Filtered Back-Projection, Feldkamp David Kress algorithm, etc., reconstruct images from measured projection data using deterministic formulas that usually approximate an algebraic inversion of a CT projection transform. The projection transform is a mathematical formula that describes the signal or photon count recorded by the detector array as a function of the volumetric density image.
Analytical image reconstruction algorithms are commonly used in CT applications due to their speed, predictable and controllable properties, and/or ease of implementation. However, analytical algorithms have several limitations. For example, this approach assumes noise-free, ideal “line integral” projection data without inconsistencies due to the physics of x-ray propagation and/or acquisition. Additionally, because image voxels are reconstructed independently from input projection data, constraints are imposed on data acquisition protocols and/or detector array design (e.g., by not allowing for missing data), for example. Data inconsistencies and imperfections (e.g., such as due to beam hardening) may lead to image artifacts. Further, analytic image reconstruction algorithms generally treat all measured photon counts uniformly regardless of the validity of these counts, resulting in suboptimal noise characteristics, for example, and generally provide merely approximations for some conversion operations, resulting in geometric artifacts, for example.
Iterative image reconstruction algorithms reconstruct an image through successive image refinements, so that expected (e.g., synthesized) projections computed from the reconstructed image substantially match measured projections. At respective iterations, the iterative algorithm forward projects the image, computes synthesized projections, compares the synthesized projection to the measured projection data, and refines the image based upon the difference(s) between the synthesized and measured projection data. It will be appreciated that this process may be repeated until a desired outcome has been reached (e.g., the process has been repeated 20 times, the synthesized projection data is within a particular tolerance of the projection data, etc.).
While iterative image reconstruction algorithms generally have a higher computation complexity, there are numerous benefits to iterative image reconstruction algorithms over analytical image reconstruction algorithms in CT applications. For example, because no mathematical inversion is performed in an iterative algorithm, approximations that generate geometric artifacts are generally avoided. Additionally, the iterative process generally improves noise performance (e.g., iterative reconstruction algorithms can deliver optimal noise performance). Moreover, because iterative image reconstruction algorithms generally do not impose strict rules on data sampling and/or detector geometry, iterative image reconstruction algorithms can be applied to nonstandard data acquisition geometries and/or missing data configurations, for example. Iterative image reconstruction algorithms can also reduce the effects of data imperfections (e.g., such as due to beam hardening and/or other physics effects) and/or improve image quality by including prior information related to properties of the image, such as image non-negativity, for example.